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Found 5 papers, 2 papers with code
Find Your Friends: Personalized Federated Learning with the Right Collaborators
In the traditional federated learning setting, a central server coordinates a network of clients to train one global model.
Short and Sparse Deconvolution --- A Geometric Approach
Short-and-sparse deconvolution (SaSD) is the problem of extracting localized, recurring motifs in signals with spatial or temporal structure.
Short-and-Sparse Deconvolution -- A Geometric Approach
This paper is motivated by recent theoretical advances, which characterize the optimization landscape of a particular nonconvex formulation of SaSD.
On the Global Geometry of Sphere-Constrained Sparse Blind Deconvolution
Blind deconvolution is the problem of recovering a convolutional kernel $\boldsymbol a_0$ and an activation signal $\boldsymbol x_0$ from their convolution $\boldsymbol y = \boldsymbol a_0 \circledast \boldsymbol x_0$.
Geometry and Symmetry in Short-and-Sparse Deconvolution
We study the $\textit{Short-and-Sparse (SaS) deconvolution}$ problem of recovering a short signal $\mathbf a_0$ and a sparse signal $\mathbf x_0$ from their convolution.
